What is the difference between A2C and running an agent in an environment vector?
I believe if you run a single agent in multiple parallel environments many times you will get similar actions in similar states, the reason behind multiple agents is that you will have different agents with different parameters and you can also have different explicit exploration policies so your exploration will be better and you will learn more from environment (see more state space). With single agent you can't really achieve that, you would have a single exploration policy, single parameter set for the agent and most of the time you would be seeing similar states (at least after a while). You would be speeding up your learning process but that's just because you're running multiple environments in parallel (compared to the regular actor-critic or Q-learning). I think quality of learning would be better with multiple different actors.